Résultats de recherche pour « Zero-Touch Network »

In modern telecommunication sectors, zero touch (Zero-Touch) networks are a novel idea. A combination of quick, context-aware network and service configuration, adaptable new service development, and dynamic, effective resource allocation are required for everyone. An innovative telecommunication management software, Zero-Touch Networks (ZTNs), were released to address these complicated conditions. The primary objective of the zero-touch network is autonomous operation, which is governed by higher layers of policies and regulations and allows for self-configuration, self-monitoring, self-healing and self-optimization without the need for human interaction. This conceptual study document is based on white and grey literature in the Google and Web of Science databases. Text, image, audio and video formats all existed for the data. The manuscripts examined the theories, enabling technologies, problems and difficulties associated with zero touch networks. The primary goal of this manuscript is to provide an overview of ZTN so that aspiring academics, researchers, students and businesspeople can profit from it.

The recent technological advancements in wireless communication systems and the Internet of Things (IoT) have accelerated the development of zero touch networks (ZTNs). ZTNs provide self-monitoring, self-configuring, and automated service-level policies that cannot be fulfilled by the traditional network management and orchestration approaches. Despite the hype, the majority of data exchange between participating entities occurs over insecure public channels, which present a number of possible security risks and attacks. Toward this end, we first analyze the attack surface on IoT-enabled ZTNs and the inherent architectural flaws for such threats. After an overview of attack surface, this article presents a new deep-learning- and blockchain-assisted case study for secure data sharing in ZTNs. Specifically, first, we design a novel variational autoencoder (VAE) and attention-based gated recurrent units (AGRU)-based intrusion detection system (IDS) for ZTNs. Second, a novel authentication protocol that combines blockchain, smart contracts (SCs), elliptic curve cryptography (ECC), and a proof of authority (PoA) consensus mechanism is developed to improve secure data sharing in ZTNs. The extensive experimental results show the effectiveness of the proposed approach. Lastly, this work discusses critical issues, opportunities, and open research directions to solve these challenges.

Faced with the rapid increase in smart Internet-of-Things (IoT) devices and the high demand for new business-oriented services in the fifth-generation (5G) and beyond network, the management of mobile networks is getting complex. Thus, traditional Network Management and Orchestration (MANO) approaches cannot keep up with rapidly evolving application requirements. This challenge has motivated the adoption of the Zero-touch network and Service Management (ZSM) concept to adapt the automation into network services management. By automating network and service management, ZSM offers efficiency to control network resources and enhance network performance visibility. The ultimate target of the ZSM concept is to enable an autonomous network system capable of self-configuration, self-monitoring, self-healing, and self-optimization based on service-level policies and rules without human intervention. Thus, the paper focuses on conducting a comprehensive survey of E2E ZSM architecture and solutions for 5G and beyond networks. The article begins by presenting the fundamental ZSM architecture and its essential components and interfaces. Then, a comprehensive review of the state-of-the-art for key technical areas, i.e., ZSM automation, cross-domain E2E service lifecycle management, and security aspects, are presented. Furthermore, the paper contains a summary of recent standardization efforts and research projects towards the ZSM realization in 5G and beyond networks. Finally, several lessons learned from the literature and open research problems related to ZSM realization are also discussed in this paper.

The combination of network softwarization with network slicing enables the provisioning of very diverse services over the same network infrastructure. However, it also creates a complex environment where the orchestration of network resources cannot be guided by traditional, human-in-the-loop network management approaches. New solutions that perform these tasks automatically and in advance are needed, paving the way to zero-touch network slicing. In this paper, we propose AZTEC, a data-driven framework that effectively allocates capacity to individual slices by adopting an original multi-timescale forecasting model. Hinging on a combination of Deep Learning architectures and a traditional optimization algorithm, AZTEC anticipates resource assignments that minimize the comprehensive management costs induced by resource overprovisioning, instantiation and reconfiguration, as well as by denied traffic demands. Experiments with real-world mobile data traffic show that AZTEC dynamically adapts to traffic fluctuations, and largely outperforms state-of-the-art solutions for network resource orchestration.

The zero-touch network and service management (ZSM) paradigm, a direct response to the increasing complexity of communication networks, is a problem-solving approach. In this article, taking advantage of recent advances in generative artificial intelligence, we introduce the network configuration generator (LLM-NetCFG) that architects ZSM configuration agents by large language models. LLM-NetCFG can automatically generate configurations, verify them, and configure network devices based on intents expressed in natural language. We also show the automation and verification of network configurations with minimum human intervention. Moreover, we explore the opportunities and challenges of integrating LLM in functional areas of network management to fully achieve ZSM.

Autonomous management capability is the main pillar for paving Zero-touch Networks and efficiently deliver and operate use cases under the light of 5G requirements. To this end, Closed Control Loop (CCL), Intent-Based Networking (IBN), and Machine Learning (ML) are regarded as enablers to automatically executed all operational processes, ideally without human intervention. In this context, the ETSI Zero-touch network and Service Management (ZSM) framework specifies an end-to-end network and service management reference architecture for managing the full lifecycle of services. However, the whole process of service monitoring is not yet well-consolidated in ETSI ZSM. In this work, we propose the Monitoring Model Generator (MMG) component to automatically construct templates for service monitoring. MMG implements a novel methodology where service deployment models and standard information models are used as inputs to generate a high-level monitoring template, called Service Monitoring Model (SMM) and built upon an ontology-based schema based on the Resource Description Framework (RDF) vocabulary. We present a proof of concept implementation along with an experimental functional validation of the MMG and using RDF data in turtle syntax and format. The resulting monitoring models are then used to define actual monitoring KPIs and construct management policies in a control loop architecture.

The exponential growth of mobile applications and services during the last years has challenged the existing network infrastructures. Consequently, the arrival of multiple management solutions to cope with this explosion along the end-to-end network chain has increased the complexity in the coordinated orchestration of different segments composing the whole infrastructure. The Zero-touch Network and Service Management (ZSM) concept has recently emerged to automatically orchestrate and manage network resources while assuring the Quality of Experience (QoE) demanded by users. Machine Learning (ML) is one of the key enabling technologies that many ZSM frameworks are adopting to bring intelligent decision making to the network management system. This paper presents a comprehensive survey of the state-of-the-art application of ML-based techniques to improve ZSM performance. To this end, the main related standardization activities and the aligned international projects and research efforts are deeply examined. From this dissection, the skyrocketing growth of the ZSM paradigm can be observed. Concretely, different standardization bodies have already designed reference architectures to set the foundations of novel automatic network management functions and resource orchestration. Aligned with these advances, diverse ML techniques are being currently exploited to build further ZSM developments in different aspects, including multi-tenancy management, traffic monitoring, and architecture coordination, among others. However, different challenges, such as the complexity, scalability, and security of ML mechanisms, are also identified, and future research guidelines are provided to accomplish a firm development of the ZSM ecosystem.

The foreseen complexity in operating and managing 5G and beyond networks has propelled the trend toward closed-loop automation of network and service management operations. To this end, the ETSI Zero-touch network and Service Management (ZSM) framework is envisaged as a next-generation management system that aims to have all operational processes and tasks executed automatically, ideally with 100 percent automation. Artificial Intelligence (AI) is envisioned as a key enabler of self-managing capabilities, resulting in lower operational costs, accelerated time-tovalue and reduced risk of human error. Nevertheless, the growing enthusiasm for leveraging AI in a ZSM system should not overlook the potential limitations and risks of using AI techniques. The current paper aims to introduce the ZSM concept and point out the AI-based limitations and risks that need to be addressed in order to make ZSM a reality.

With the continued growth of IoT devices and their deployment, manually managing and connecting them is impractical and presents multiple challenges. To that end, Zero Touch Networks (ZTNs) that rely on software-based modules instead of dedicated propriety hardware become a viable potential solution. The overall aim of zero-touch networks is for machines to learn how to become more autonomous so that we can delegate complex, mundane tasks to them. Thus, Zero Touch Networks are able to monitor networks and services and act on faults with minimal (if any) human intervention, including the early detection of emerging problems, autonomous learning, autonomous scaling, remediation, decision making, and support of various optimization objectives. As a result, Zero Touch Networks are able to offer self-serving, self-fulfilling, and self-assuring operations.